Ground-mounted photovoltaic potential in Canada based on proximity to the electrical grid: Prince Edward Island case study
Bibliographic record
Abstract
To meet Canada’s target of net-zero greenhouse gas emissions by 2050, installed renewable energy capacity needs to be scaled up quickly. While scaling up, connecting renewable energy systems to the electrical grid will be a critical factor. This article presents a methodology for estimating ground-mounted photovoltaic (PV) potential as a function of distance from the electrical grid, for any region in Canada or elsewhere. The Canadian landmass is mapped on a pixelated spatial grid and each pixel is characterized by four features, namely PV yield, distance from the electrical grid, slope and land cover class. Pixels are then included or excluded from PV potential estimates based on viability tests reflecting suitability for PV deployment. This method is applied to the province of Prince Edward Island (PEI) as a case study. Results highlight the importance of cropland and agrivoltaic applications to ground-mounted PV potential, with potential being roughly 13 times greater when cropland is included. However, even excluding cropland, this analysis suggests that the PV capacities identified in scenarios for PEI in 2050 could be deployed entirely within 1 km of the electrical grid. More granular analyses will be conducted in the future to refine these estimates considering additional constraints faced by PV developers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".